Managing non-destructive evaluation data

ABSTRACT

Methods manage non-destructive evaluation (“NDE”) data. NDE data for an asset is received and at least one alignment algorithm to align the NDE data to a simulated model associated therewith is determined. The NDE data is automatically aligned to the simulated model, a display representation that visually represents the aligned NDE data on the simulated model is generated, and information about the aligned NDE data is exported. Additionally, second NDE data associated with the at least a portion of the asset may also be received, at least one alignment algorithm to align the data determined, and the second NDE data aligned. Respective indications associated with the first and second NDE data may be determined and visually represented on the simulated model. Moreover, a shot descriptor file may be analyzed to determine whether additional NDE data is required to complete an alignment of NDE data.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. application Ser. No.12/403,274 to Joseph M. Kesler et al., entitled “MANAGINGNON-DESTRUCTIVE EVALUATION DATA,” filed Mar. 12, 2009 (SDL/08), whichapplication is incorporated by reference herein.

FIELD OF THE INVENTION

The present invention relates to computing systems, and moreparticularly to the management and automatic alignment ofnon-destructive evaluation (“NDE”) data.

BACKGROUND OF THE INVENTION

Non-destructive Evaluation and Inspection (“NDE/I”) technologiesgenerally provide ways to nondestructively scan, image, sense orotherwise evaluate characteristics of materials and/or components. Inparticular, NDE/I technologies may be used to detect minute flaws anddefects in those materials and/or component parts. As such, NDE/Itechnologies have become increasingly used to help assure structural andfunctional integrity, safety, and cost effective sustainment of variousassets, during both initial manufacture and operational service. Morespecifically, NDE/I technologies have been increasingly used indetermining the wear and tear of assets that are pushed to theirphysical limits, such as military vehicles. As the average age of thevarious assets increases, particularly beyond the originallycontemplated design life, the importance of using NDE/I technologies todetect structural damage before that damage advances to structuralfailure is paramount.

Non-destructive evaluation (“NDE”) data is often gathered from NDE datacollection devices and may include x-ray images of at least a portion ofan asset, such as the wing of an aircraft. By analyzing a dataset of NDEdata, defects or other structural irregularities of the asset at thelocation associated with that dataset of NDE data can be detected.However, this NDE data is typically difficult to manage and handle. Forexample, the NDE data is often large in size, associated with merely aportion of the asset, and also must be matched with a particularlocation on the asset. To determine wear and tear, structural damageand/or other irregularities of an entire component of an asset mayrequire the analysis of tens (if not hundreds) of individual datasets ofNDE data. This results in numerous datasets of NDE data for each asset,and thus even more datasets of NDE data for a fleet of assets. As eachdataset of NDE data is often inspected, this results in large amounts ofdata that are difficult to categorize and otherwise analyze in whole.Moreover, the NDE data may be discarded after it has been analyzed, andthus there is often little NDE data for an asset over time. Thus, when apotential problem is indicated, it is often difficult to track thatindication on an asset through time, analyze that indication in relationto other indications of the asset, and analyze that indication inrelation to indications of a plurality of similar assets.

As the amount of NDE data increases, so do associated costs and needsfor users trained to perform inspections. Although NDE data collectiondevices may produce digital data, the digital data is being generatedwithout systems in place to manage and archive the collectedinformation. Moreover, the analysis of NDE data is often laborious andcrude. Some conventional systems receive NDE data and align it to asimulated model of a portion of an asset through the use of manualtools. However, these manual tools require human interaction andgenerally require a user experienced with that NDE data and/or asset toalign and analyze that NDE data. Although some conventional systems haveused automatic alignment of the NDE data, these methods often fail as amethod of alignment for one dataset of NDE data is typically not usefulfor another dataset of NDE data. Thus, conventional systems aretypically unable to align datasets of NDE data that are in turnassociated multiple modalities (e.g., datasets of NDE data captured withvarious NDE data collection devices). This often has the effect of tyingparticular method of alignments to particular NDE data collectiondevices, and thus increases the cost of NDE data capture and analysis.

Consequently, there is a continuing need to manage datasets of NDE dataof an asset or fleet of assets over time, as well as a continuing needto support the alignment of multiple modalities of NDE data in anextensible platform that supports NDE data fusion.

SUMMARY OF THE INVENTION

Embodiments of the invention provide for a method, apparatus, andprogram product to manage non-destructive evaluation (“NDE”) dataassociated with at least a portion of an asset. In particular,embodiments of the invention provide for a method to manage NDE dataassociated with at least a portion of an asset in a system of the typethat includes at least one processing unit and a memory. The methodcomprises receiving NDE data for at least a portion of an asset,including receiving inspection information associated with the at leasta portion of the asset. The method further comprises determining atleast one alignment algorithm to align the NDE data to a simulated modelof the at least a portion of the asset based upon at least one of theNDE data and the inspection information. The method further comprisesautomatically aligning the NDE data to the simulated model with the atleast one alignment algorithm and generating a display representationthat visually represents the aligned NDE data on the simulated model,and exporting information associated with the aligned NDE data foranalysis.

In additional embodiments, a method of managing NDE data comprisesreceiving first NDE data for at least a portion of an asset, includingreceiving first inspection information associated with the at least aportion of the asset, as well as receiving second NDE data for the atleast a portion of the asset, including receiving second inspectioninformation associated with the at least a portion of the asset. Themethod further comprises determining a first alignment algorithm toalign the first NDE data to a simulated model of the at least a portionof the asset based upon at least one of the first NDE data and the firstinspection information and determining a second alignment algorithm toalign the second NDE data to the simulated model of the at least aportion of the asset based upon at least one of the second NDE data andthe second inspection information. The method additionally comprisesautomatically aligning the first NDE data and second NDE data to thesimulated model with the respective first alignment algorithm and secondalignment algorithm, determining a first indication of a first potentialproblem associated with the first NDE data based upon the firstinspection information, and determining a second indication of a secondpotential problem associated with the second NDE data based upon thesecond inspection information. The method further comprises generating adisplay representation that visually represents the first indication andthe second indication on the simulated model.

In still further embodiments, a method of managing NDE data comprisesreceiving first NDE data for at least a portion of a first asset,including receiving first inspection information associated with the atleast a portion of the first asset as well as receiving second NDE datafor the at least a portion of a second asset, including receiving secondinspection information associated with the at least a portion of thesecond asset. The method further comprises retrieving a simulated modelassociated with the at least a portion of the first asset and the atleast a portion of the second asset, determining a first alignmentalgorithm to align the first NDE data to the simulated model based uponat least one of the first NDE data and the first inspection information,and determining a second alignment algorithm to align the second NDEdata to the simulated model based upon at least one of the second NDEdata and the second inspection information. The method additionallycomprises automatically aligning the first NDE data and second NDE datato the simulated model with the respective first alignment algorithm andsecond alignment algorithm, determining a first indication of a firstpotential problem associated with the first NDE data based upon thefirst inspection information, and determining a second indication of asecond potential problem associated with the second NDE data based uponthe second inspection information. The method further comprisesgenerating a display representation that visually represents the firstindication and the second indication on the simulated model

In additional embodiments, a method of managing NDE data comprisesreceiving NDE data for at least a portion of an asset, includingreceiving inspection information associated with the at least a portionof the asset, and selecting a shot descriptor data structure from amonga plurality of shot descriptor data structures based upon at least oneof the NDE data and the inspection information. The method furthercomprises determining whether additional NDE data is required tocomplete an alignment of the NDE data to a simulated model of the atleast a portion of the asset based upon the shot descriptor datastructure and providing a message that additional NDE data is requiredto complete the alignment of the NDE data to the simulated model inresponse to determining additional NDE data is required to complete thealignment of the NDE data to the simulated model.

In another embodiment, a method of managing NDE data comprisesretrieving NDE data for at least a portion of an asset, the NDE datahaving previously been aligned to a simulated model of the at least aportion of the asset, and exporting information associated with theretrieved NDE data for analysis.

These and other advantages will be apparent in light of the followingfigures and detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate embodiments of the invention and,together with a general description of the invention given above and thedetailed description of the embodiments given below, serve to explainthe principles of the invention.

FIG. 1 is a diagrammatic illustration of a computer configured toaccomplish the management of non-destructive evaluation (“NDE”) dataconsistent with embodiments of the invention;

FIG. 2 is a diagrammatic illustration of an alternative embodiment of acomputer configured to accomplish the management of NDE data consistentwith alternative embodiments of the invention;

FIG. 3 is a diagrammatic illustration of the primary components of anapplication configured to manage NDE data in the computer of FIG. 1 orFIG. 2;

FIG. 4 is a diagrammatic illustration of a data structure and theprimary components thereof of the computer of FIG. 1 or FIG. 2configured to store data and/or information for the management of NDEdata;

FIG. 5 is a flowchart illustrating a sequence of operations that may beexecuted by the application of FIG. 3 to accomplish the management ofNDE data;

FIG. 6 is a flowchart illustrating a sequence of operations that may beexecuted by the application of FIG. 3 generally illustrating themanagement of NDE data;

FIG. 7 is a flowchart illustrating in more detail the tag translationprocess of FIG. 6;

FIG. 8 is a flowchart illustrating in more detail the sessionassociation process of FIG. 6;

FIG. 9 is a flowchart illustrating in more detail the alignmentalgorithm selection process and automatic alignment process of FIG. 6;

FIG. 10 is a flowchart illustrating a sequence of operations that may beexecuted by the application of FIG. 3 to determine missed coverage of atleast a portion of a simulated model;

FIG. 11 is a flowchart illustrating a sequence of operations that may beexecuted by the application of FIG. 3 to align two datasets of NDE datawith each other and determine the location of an indication;

FIG. 12 is a flowchart illustrating a sequence of operations that may beexecuted by the application of FIG. 3 to maintain data integrity of NDEdata captured from an NDE data collection device;

FIG. 13 is a flowchart illustrating a sequence of operations that may beexecuted by the application of FIG. 3 to retrieve NDE data associatedwith an first indication in response to the selection of that firstindication from among a plurality of indications;

FIG. 14 is a flowchart illustrating a sequence of operations that may beexecuted by the application of FIG. 3 to filter a plurality ofindications;

FIG. 15 is a flowchart illustrating a sequence of operations that may beexecuted by the application of FIG. 3 to sample NDE data and produce aplurality of resolutions of that NDE data;

FIG. 16 is a flowchart illustrating a sequence of operations that may beexecuted by the application of FIG. 3 to display a dataset of NDE dataor a sub-dataset thereof;

FIG. 17 is a flowchart illustrating a sequence of operations that may beexecuted by the application of FIG. 3 to assign a location descriptor toat least a portion of an asset and translate location informationassociated therewith;

FIG. 18 is a flowchart illustrating a sequence of operations that may beexecuted by the application of FIG. 3 to standardize text stringsassociated with NDE data and/or inspection information;

FIG. 19 is a flowchart illustrating a sequence of operations that may beexecuted by the application of FIG. 3 to display NDE data of a firstmodality as NDE data of a second modality;

FIG. 20 is a flowchart illustrating a sequence of operations that may beexecuted by the application of FIG. 3 to display indications from aplurality of datasets of NDE data associated with a similar portion of aplurality of assets;

FIG. 21 is a flowchart illustrating a sequence of operations that may beexecuted by the application of FIG. 3 to display indications from aplurality of datasets of NDE data associated with at least a portion ofa particular asset;

FIG. 22 is a flowchart illustrating a sequence of operations that may beexecuted by the application of FIG. 3 to determine the uncertainty of alocation on a simulated model;

FIG. 23 is a flowchart illustrating a sequence of operations that may beexecuted by the application of FIG. 3 to determine the uncertainty of alocation on a simulated model due to the inspection process, and inparticular due to the various modalities associated with NDE data;

FIG. 24 is a flowchart illustrating a sequence of operations that may beexecuted by the application of FIG. 3 to display a plurality ofindications of portions of at least one asset that are substantiallystructurally similar, but that may be located at different locations onthe at least one asset or that are oriented in different mannersrespective to each other;

FIG. 25 is a flowchart illustrating a sequence of operations that may beexecuted by the application of FIG. 3 to locally deform at least aportion of NDE data;

FIG. 26 is a display representation of at least a portion of a datasetof NDE data consistent with embodiments of the invention;

FIG. 27 is a display representation of a simulated model consistent withembodiments of the invention;

FIG. 28 is a display representation of the NDE data of FIG. 26 rotatedto align with the simulated model of FIG. 27;

FIG. 29 is a display representation of the aligned NDE data of FIG. 28on the simulated model of FIG. 27;

FIG. 30 is a display representation that illustrates an uncertainty of adetermined location for an indication on the simulated model of FIG. 27by displaying a probability density of the location at the location;

FIG. 31 is a display representation that illustrates an uncertainty of adetermined location for an indication on the simulated model of FIG. 27by adjusting a color component of at least a portion of the simulatedmodel at the location;

FIG. 32 is a display representation that illustrates a displayrepresentation of the simulated model of FIG. 27 in which a colorcomponent of at least a portion of a background of the displayrepresentation has been adjusted to indicate a lapse in coverage ofaligned NDE data with the simulated model;

FIG. 33 is a display representation that illustrates a displayrepresentation 670 in which a color component of at least a portion ofthe simulated model of FIG. 27 has been adjusted to indicate a lapse incoverage of aligned NDE data with the simulated model;

FIG. 34 is a display representation of the simulated model of FIG. 27 inwhich a plurality of indications have been visually represented;

FIG. 35 is a display representation of the simulated model of FIG. 27 inwhich a first plurality of indications have been visually represented;

FIG. 36 is a display representation of a second simulated model in whicha second plurality of indications have been visually representedconsistent with embodiments of the invention;

FIG. 37 is a display representation of the second plurality ofindications visually represented on the simulated model of FIG. 27;

FIG. 38 is a display representation of the first plurality ofindications of FIG. 35 and the second plurality of indications of FIG.37 visually represented on the simulated model of FIG. 27;

FIG. 39 is a diagrammatic illustration of at least a portion of a fanblade of a jet engine;

FIG. 40 is a diagrammatic illustration of the fan blade of FIG. 39 andthe location of a CT slice taken through the fan blade consistent withembodiments of the invention;

FIG. 41 is a display representation of at least a portion of a datasetof NDE data associated with the CT slice of FIG. 40;

FIG. 42 is a diagrammatic illustration of a projection of the NDE dataof FIG. 41 through a second dataset of NDE data associated with the fanblade of FIG. 39 and a three-dimensional simulated model of the fanblade of FIG. 39;

FIG. 43A and FIG. 43B are respective projections of the NDE data of FIG.41 through the second dataset of NDE data and/or the simulated model ofFIG. 42;

FIG. 44 is a display representation of at least a portion of a datasetof distorted NDE data consistent with embodiments of the invention;

FIG. 45 is a display representation of the distorted NDE data of FIG. 44aligned with and on the simulated model of FIG. 27;

FIG. 46 is a display representation of the distorted NDE data of FIG. 44after at least a portion thereof has been adjusted consistent withembodiments of the invention; and

FIG. 47 is a display representation of the adjusted NDE data of FIG. 46aligned with and on the simulated model of FIG. 27.

It should be understood that the appended drawings are not necessarilyto scale, presenting a somewhat simplified representation of variouspreferred features illustrative of the basic principles of theinvention. The specific design features of the sequence of operations asdisclosed herein, including, for example, specific dimensions,orientations, locations, and shapes of various illustrated components,will be determined in part by the particular intended application anduse environment. Certain features of the illustrated embodiments mayhave been enlarged or distorted relative to others to facilitatevisualization and clear understanding.

DETAILED DESCRIPTION

Embodiments of the invention provide for a method, apparatus, andprogram product to manage non-destructive evaluation (“NDE”) dataassociated with at least a portion of an asset. In particular,embodiments of the invention provide for aligning NDE data with asimulated model associated with the at least a portion of the asset forinspection thereof. In some embodiments, the NDE data is associated withinspection information. The inspection information may include at leastone indication of a potential problem and a location thereof on the NDEdata. In some embodiments, the at least one indication is aligned to thesimulated model and viewed. In additional embodiments, a plurality ofdatasets of NDE data (e.g., a plurality of individual instances of NDEdata), at least some of which is associated with inspection information,is aligned to the simulated model. As such, indications in turnassociated with the inspection information of the plurality of datasetsmay be viewed for trends of indications, including trends of indicationsof one or more assets over time.

Hardware and Software Environment

Turning to the drawings, wherein like numbers denote like partsthroughout the several views, FIG. 1 illustrates a hardware and softwareenvironment for an apparatus 10 for managing non-destructive evaluationdata consistent with embodiments of the invention. Apparatus 10, forpurposes of this invention, may represent any type of computer,computing system, server, disk array, or programmable device such as amulti-user computer, single-user computer, handheld device, networkeddevice, mobile phone, gaming system, etc. Apparatus 10 may beimplemented using one or more networked computers, e.g., in a cluster orother distributed computing system. Apparatus 10 will be referred to as“computer” for brevity sake, although it should be appreciated that theterm “apparatus” may also include other suitable programmable electronicdevices consistent with the invention.

Computer 10 typically includes at least one central processing unit(“CPU”) 12 coupled to a memory 14. Each CPU 12 may be one or moremicroprocessors, micro-controllers, field programmable gate arrays, orASICs, while memory 14 may include random access memory (RAM), dynamicrandom access memory (DRAM), static random access memory (SRAM), flashmemory, and/or another digital storage medium. As such, memory 14 may beconsidered to include memory storage physically located elsewhere incomputer 10, e.g., any cache memory in the at least one CPU 12, as wellas any storage capacity used as a virtual memory, e.g., as stored on amass storage device, a computer, or another controller coupled tocomputer 10 through a network interface 16 (illustrated as, andhereinafter, “network I/F” 16) by way of a network 18. The computer mayinclude a mass storage device 20, which may also be a digital storagemedium, and in specific embodiments includes at least one hard diskdrive. Additionally, mass storage device 20 may be located externally tocomputer 10, such as in a separate enclosure or in one or more networkedcomputers 22, one or more networked storage devices 24 (including, forexample, a tape drive), and/or one or more other networked devices 26(including, for example, a server). The computer may communicate withnetworked computers 22, networked storage devices 24, and/or networkeddevices 26 through network 18.

The computer 10 may also include peripheral devices connected to thecomputer through an input/output device interface 28 (illustrated as,and hereinafter, “I/O I/F” 28). In particular, the computer 10 mayreceive data from a user through at least one user interface 30(including, for example, a keyboard, mouse, and/or other user interface)and/or output data to a user through at least one output device 32(including, for example, a display, speakers, and/or another outputdevice). Moreover, in some embodiments, the I/O I/F 28 communicates witha device that includes a user interface 30 and at least one outputdevice 32 in combination, such as a touchscreen (not shown).

Computer 10 may be under the control of an operating system 34 andexecute or otherwise rely upon various computer software applications,components, programs, files, objects, modules, etc. (illustrated as“application” 36) for managing non-destructive evaluation (“NDE”) dataconsistent with embodiments of the invention as described herein.Moreover, computer 10 may include at least one data structure 38 tostore various data consistent with embodiments of the invention. Thedata structure 38 in turn may include at least one database, list,array, table, data entry, file, and/or another data structure for use bythe application 36, computer 10, and/or components thereof. It will beappreciated that various applications, components, programs, objects,modules, etc. may also execute on one or more processors in anothernetworked device coupled to computer 10 via the network 18, e.g., in adistributed or client-server computing environment.

FIG. 2 is an alternative embodiment of a computing system 40 formanaging non-destructive evaluation and inspection data consistent withalternative embodiments of the invention. As illustrated, the computingsystem 40 may include a first computer 42, which may be substantiallysimilar to computer 10 illustrated in FIG. 1 and operate as anapplication, web, and/or NDE data server for the computing system 40. Inspecific embodiments, the first computer 42 may be accessed by a secondcomputer 44 through a network 46. As such, the first computer 42 mayprovide access to the application 36 and/or at least one data structure38 for that second computer 44 to access, view, add, modify, and/ordelete NDE data. In some embodiments, the network 46 is a wide areanetwork (such as, for example, the internet) such that the firstcomputer 42 communicates with the second computer 44 through a wide areanetwork using a secure TCP/IP connection (e.g., a secure internetconnection), while in alternative embodiments network 46 is a local areanetwork.

In addition to second computer 44, the computing system may include atleast one storage server 48 and at least one NDE data collection device50 which may in turn be in communication with the network 46. Thestorage server 48 may be configured with the at least one data structure38 consistent with embodiments of the invention. In some embodiments,and as illustrated in FIG. 2, the at least one data structure 38 is adatabase. The storage server 48 may be accessed by the first computer 42in response to a request from second computer 44. The storage server 48may be in communication with the first computer 42 through the network46 or through a direct communication link as at 52. In some embodiments,the at least one storage server 48 is configured to store NDE data andinspection information associated therewith in the at least one datastructure 38. The at least one storage server 48 may be configured as apicture archiving and communication system (“PACS”) and store the NDEdata as a digital image in the digital imaging and communication innon-destructive evaulation (“DICONDE”) standard as is well known in theart.

NDE data from at least a portion of an asset 54 may be captured by theat least one NDE data collection device 50 and provided to the firstcomputer 42, the second computer as at 56, and/or the storage server 48.The NDE data may be in various modalities, including a one-dimensionalrepresentation (e.g., a waveform), a two-dimensional representation(e.g., an image), a three-dimensional representation (e.g., a volumetricimage), temporal data, text, an audio recording, a video recording, abinary representation (e.g., for example, a logic high signal for apassing condition or a logic low signal for a failing condition), orcombinations thereof. Similarly, the simulated model may be in variousmodalities, including a one-dimensional representation (e.g., awaveform), a two-dimensional representation (e.g., an image), athree-dimensional representation (e.g., a volumetric image), temporaldata, or combinations thereof. In specific embodiments, the simulatedmodel is a second dataset of NDE data.

The NDE data collection device 50 may include one or more cameras (e.g.,to capture still images for visualization, videos for visualization,and/or for sherography, etc.), thermograpic cameras (e.g., to capture athermographic image), borescopes, fiberscopes, x-ray machines (e.g., tocapture still images, to use with computed radiography, to use withdirect and/or digital radiography, etc.), ultrasound machines, CTscanners, MRI machines, eddy current detectors, liquid penetrantinspection systems, and/or magnetic-particle inspection systems. Thus,the NDE data may be captured as a specific type of NDE data associatedwith a respective type of NDE data collection device 50. The asset 54may be a machine, component, or other physical object, and in someembodiments may be an aircraft (e.g., a drone, a balloon, an airplane, ahelicopter, etc.), a land vehicle (e.g., a trailer, a car, a truck, atractor, a tank, a snowmobile, etc.), a sea vehicle (e.g., a skiff, apersonal watercraft, a boat, a speed-boat, a yacht, a cruiser, adestroyer, etc.), a pipeline, an industrial plant (e.g., a power plant,a chemical plant, an electrical plant, etc.), a bridge, and/or acomponent thereof. In specific embodiments, the asset 54 is a militaryaircraft.

NDE data may be associated with inspection information that associatesthe NDE data with particular information that may be useful to align theNDE data, indicate potential problems, and/or otherwise provide dataabout the portion of the asset. In addition to capturing the NDE data,the at least one NDE data collection device 50 may be configured tocapture inspection information associated with that captured NDE data.For example, the NDE data collection device 50 may include a userinterface (not shown) in which to enter inspection information. In thoseembodiments, the inspection information may include data associated witha location of the asset 54 from which the NDE data was captured, anidentification of the asset 54, a history of the asset 54, a time atwhich the NDE data was captured, a date at which the NDE data wascaptured, an identification of an NDE session associated with the NDEdata, an annotation associated with the NDE data (e.g., such as anannotation that includes an indication of a potential problem), anidentification of an inspector associated with the NDE data, anidentification of a series of NDE data in which the NDE data wascaptured, an identification of the location of the NDE data in theseries of NDE data, an orientation associated with the NDE data, aunique identification of the NDE data, an identification of the modalityof NDE data collection device 50 used to capture the NDE data, and/orcombinations thereof. The inspection information may be determinedautomatically, captured by the first computer 42, and/or captured by thesecond computer 44 before, during, or after the capture of the NDE data.For example, NDE data may captured by the at least one NDE datacollection device 50 and viewed on the second computer 44. At least aportion of the inspection information for the NDE data may then beentered by a user at the second computer.

A “session” may include an inspection session during which NDE data iscaptured from an asset 54. A session may include the capture of aplurality of datasets of NDE data, and in particular a sequence tocapture those datasets of NDE data. During a session, at least onedataset of NDE data associated with the asset 54 may be captured with atleast one NDE data collection device 50, and each dataset of NDE datamay be associated with at least a portion of the asset 54 as well as thetime at which that NDE data was captured. Thus, NDE data may beassociated with a session and, in some embodiments of the invention, NDEdata from the same session may be aligned to a simulated model toprovide a snapshot of at least a portion of an asset 54 at a particulartime. For example, a session may include the capture of a plurality ofdatasets of NDE data with an x-ray machine from a spar of a wing of aplane. Specifications for the session may indicate that at least sevendatasets of NDE data be captured along the length of the spar in anattempt to capture NDE data for the entire length of the spar. Aspectsof the invention provide for aligning NDE data associated with thesession, and thus aligning NDE data associated with the spar, thengenerating a display representation of the aligned NDE data to provide asnapshot of the spar at the time of the session. As a further example,aligned NDE data from previous sessions may be compared with the alignedNDE data from example session to compare the spar over time.

FIG. 3 is a diagrammatic illustration of one embodiment of a pluralityof components of an application 36 consistent with embodiments of theinvention. The application 36 may include various components, modules,or program code to manage NDE data and/or inspection information. Inparticular, the application 36 may include a translation component 60,an automatic alignment engine 61, a session identification component 62,a user and/or display interface 63, a report generator 64, an annotationextraction component 65, a filter and/or searching component 66, and/oradditional components with which to manage NDE data. The translationcomponent 60 may be configured to translate information associated withNDE data and/or inspection information into standardized information.The standardized information, in turn, may be utilized to assist in thealignment of the NDE data. For example, the standardized information mayindicate an asset 54 with which the NDE data and/or inspectioninformation is associated, indicate a portion of the asset 54 with whichthe NDE data and/or inspection information is associated, indicate alocation (e.g., for example, an approximate location) of the at least aportion of the asset with which NDE data and/or inspection informationis associated, be utilized to help choose a shot descriptor file toassociate with the NDE data and/or otherwise be utilized to assist inthe alignment of the NDE data and/or indications of the inspectioninformation. The automatic alignment engine 61 may be used to align NDEdata to a simulated model, while the session identification component 62may be used to determine a session associated with the NDE data. Theuser and/or display interface 63 may be used to provide a displayrepresentation for the application 36, the NDE data, the simulatedmodel, and/or the golden NDE data. In particular, the user and/ordisplay interface 63 may be used to provide a display representation forthe NDE data, the simulated model, and/or the golden NDE image inresponse to an automatic alignment and/or for manual alignment. Thereport generator 64 may be used to generate a report about the NDE data,and thus a report about at least a portion of the asset 54. Moreover,the application 36 may include an annotation extraction component 65that may be configured to extract and standardize annotations about theNDE data. For example, an operator may input inspection informationassociated with NDE data indicating that a potential problem wasdetected, the location of the potential problem, and the type of thepotential problem. The annotation extraction component 65 may beconfigured to analyze the indication and convert annotations tostandardized annotations suitable for searching and indexing.

Additionally, the application 36 may include a filtering and/orsearching component 66 with which to filter, and/or search through, NDEdata, indications thereof, particular assets, serial numbers, inspectiontimes, etc. In particular, the filter and/or searching component may beused to filter or search through a plurality of datasets of NDE data.For example, the data structure 38 may include a plurality of datasetsof NDE data from a plurality of portions of a plurality of assets 54over a plurality of sessions. The plurality of datasets may be filteredto align only NDE data for a particular portion of a particular asset 54during a particular session to a simulated model. Thus, the portion ofthe asset 54 at a particular time (e.g., at the time of the particularsession) may be viewed. Also for example, the data structure 38 mayinclude a plurality of datasets of NDE data from a plurality of portionsof a plurality of assets 54 over a plurality of sessions. In turn, atleast a portion of the plurality of datasets of NDE data may beassociated with respective inspection information that in turn includesat least one indication of a potential problem. The plurality ofdatasets may be filtered to align indications for a particular portionof the plurality of assets on the simulated model. Thus, indications ofpotential problems (e.g., for example, cracks, corrosion, etc.)associated with a particular portion of the plurality of assets 54(e.g., for example, each asset may be a plane, and the particularportion may be a wing of the planes) over time (e.g., over the course ofthe plurality of sessions) may be viewed. Thus, a display representationthat visually represents potential problems in a specific wing across afleet of planes may be generated.

Although several components of the application 36 are illustrated inFIG. 3, one having ordinary skill in the art will appreciate that theapplication 36 may include more or fewer components without departingfrom the scope of the invention. For example, the application 36 mayinclude an HTML generator (not shown), a shot descriptor parser toaccess and search through shot descriptor data structures (not shown),and/or a another component as may be apparent to one having ordinaryskill in the art. Moreover, one having ordinary skill in the art willappreciate that the application 36 may include fewer components and oneor more of the individual components 61-66 may be separate applicationswithout departing from the scope of the invention.

FIG. 4 is a diagrammatic illustration of one embodiment of the at leastone data structure 38 consistent with embodiments of the invention. Theat least one data structure 38 may include a plurality of individualdata structures, including at least one NDE data data structure 70, atleast one inspection information data structure 72, at least onetranslation information data structure 74, at least one sessionassociation data structure 76, at least one algorithms data structure78, at least one simulated model data structure 80, at least one goldenNDE data data structure 82, at least one features data structure 84, atleast one indications data structure 86, at least one alignmentadjustment data structure 88, at least one shot descriptor datastructure 90, at least one stored alignment data structure 92 and/orcombinations thereof. Moreover, the data structure 38 may includeadditional data structures without departing from the scope of theinvention. In some embodiments, the NDE data data structure 70 isconfigured to store at least one dataset of NDE data while theinspection information data structure 72 is configured to storeinspection information associated with NDE data. The translationinformation data structure 74 may be configured with a lexicon, aplurality of semantic concepts, individualized translation information,and/or other translation information for the translation component 62 totranslate information associated with NDE data and/or inspectioninformation into standardized information. In alternative embodiments,translation information may be provided in a translation informationdata structure that may be otherwise separate from the translationinformation data structure 74, such as in a translation information fileand/or data structure supplied by a user or otherwise accessible by thetranslation component 62.

The algorithms data structure 78 may include a plurality of alignmentalgorithms from which to choose an alignment algorithm with which toautomatically align NDE data (as well as indications associated withinspection information that is in turn associated with NDE data) to asimulated model. Embodiments of the invention support multiple alignmentalgorithms to automatically align NDE data to the simulated model. Forexample, the alignment algorithms may include alignment algorithms toalign a dataset of NDE data to the simulated model through feature basedalignments, area based alignments, parallel projection algorithms and/orother manners of alignment known to one having ordinary skill in theart. For example, feature based alignment algorithms may extract featuresets from a dataset of NDE data and the simulated model and attempt toalign the two feature sets through best fit. Specifically, the featuresmay include straight-line segments, corners, points, intensity ofparticular areas, etc. The feature set of the NDE data may then becompared to the feature set of the simulated model and aligned throughbest fit to register the NDE data to the simulated model. The alignmentmay include a rotation, translation, scale transformation and/or otherregistration of the feature set and/or NDE data.

Alternatively, and also for example, area matching alignment algorithmsmay set up a matrix to compare an area of a dataset of NDE data (e.g.,the entire dataset of NDE data or a portion thereof) to an area of thesimulated model (e.g., the entire simulated model or a portion thereof).Specifically, a Fourier transform of an area of the dataset of NDE datamay be compared to a Fourier transform of an area of the simulatedmodel. A translation between the two datasets may correspond to a phaseshift between the two Fourier transforms thereof. As such, a functionmay be run on both of the Fourier transforms to align the Fouriertransforms, and thus the datasets of NDE data. For example, a crosspower spectrum of the two Fourier transforms may be taken, and aninverse Fourier transform of the cross power spectrum may provide anindication of at least one location that corresponds between the twodatasets of NDE data. The datasets may then be aligned with reference tothe at least one location. As such, the Fourier transforms may bemanipulated, processed or otherwise analyzed to provide a translation,rotation and/or scale change between the two datasets.

Also for example, the area matching algorithms may attempt to align NDEdata through mutual matching of an area of a dataset of NDE data to anarea of the simulated model. Specifically, the area matching algorithmsmay attempt to maximize mutual information between an area of the NDEdata and an area of the simulated model. Although some alignmentalgorithms are disclosed, it will be apparent to one having ordinaryskill in the art that additional alignment algorithms and alignmentmethods may be used without departing from the scope of the invention.

Additionally, the algorithms data structure 78 may include a pluralityof auditing algorithms to audit an alignment of NDE data on a simulatedmodel. The simulated model, in turn, may be stored in the simulatedmodel data structure 80. Golden NDE data, which may be a blueprint,schematic, NDE data, and/or other technical information about at least aportion of an asset 54, may be used to generate the simulated model.Alternatively, the simulated model may be supplied by a user withoutgenerating that simulated model from golden N DE data. Golden NDE datamay be stored in the golden NDE data data structure 82. In someembodiments, NDE data and/or a simulated model may be aligned with itsrespective golden NDE data.

The features data structure 84 may include data associated with aplurality of features of respective portions of an asset. For example,one portion of an asset may have a feature that may be used to align NDEdata to a simulated model. An alignment algorithm may use informationabout features of NDE data to align that NDE data to a simulated model.The indications data structure 86 may include a plurality of potentialproblems as indicated from inspection information or as determined fromthe inspection of the NDE data. The alignment adjustment data structure88 may include adjusted algorithm parameters for various alignmentalgorithms. In particular, the alignment adjustment data structure 88may include adjusted algorithm parameters as determined from input by auser in response to an alignment of NDE data to a simulated model, orthe adjusted algorithm parameters may be automatically determined by theapplication 36 and stored in the alignment adjustment data structure 88in response to an audit of the alignment of NDE data to the simulatedmodel. The shot descriptor data structure 90 may include a plurality ofshot descriptor data structures which may in turn be used to determinean alignment algorithm, if any, with which to align NDE data. The storedalignment data structure 92 may include stored parameters for aplurality of previous alignments of a respective plurality of NDE data.For example, the stored parameters may indicate input parameters for oneor more alignment algorithms as well as specify the alignment algorithmswith which to align previously aligned NDE data. Upon subsequentretrieval and/or alignment of that NDE data, the stored alignment datastructure 92 may provide parameters to align that NDE data. Referring toFIG. 4, each of the data structures 70-92 may be disposed in at leastone database, list, array, table, data entry, file, and/or another datastructure for use by the application 36, computer 10, and/or a componentthereof.

In general, the routines executed to implement the embodiments of theinvention, whether implemented as part of an operating system or aspecific application, component, algorithm, program, object, module orsequence of instructions, or even a subset thereof, will be referred toherein as “computer program code” or simply “program code.” Program codetypically comprises one or more instructions or sequence of operationsthat are resident at various times in memory and storage devices in acomputer, and that, when read and executed by at least one processor ina computer, cause that computer to perform the steps necessary toexecute steps or elements embodying the various aspects of theinvention. Moreover, while the invention has and hereinafter will bedescribed in the context of fully functioning computers and computersystems, those skilled in the art will appreciate that the variousembodiments of the invention are capable of being distributed as aprogram product in a variety of forms, and that the invention appliesregardless of the particular type of computer readable media used toactually carry out the invention. Examples of computer readable mediainclude, but are not limited to, recordable type media such as volatileand non-volatile memory devices, floppy and other removable disks, harddisk drives, tape drives, optical disks (e.g., CD-fourths, ROM's, DVD's,HD-DVD'S, Blu-Ray Discs), among others, and transmission type media suchas digital and analog communications links.

In addition, various program code described hereinafter may beidentified based upon the application or software component within whichit is implemented in specific embodiments of the invention. However, itshould be appreciated that any particular program nomenclature thatfollows is merely for convenience, and thus embodiments of the inventionshould not be limited to use solely in any specific applicationidentified and/or implied by such nomenclature. Furthermore, given thetypically endless number of manners in which computer programs may beorganized into routines, procedures, methods, modules, objects, and thelike, as well as the various manners in which program functionality maybe allocated among various software layers that are resident within atypical computer (e.g., operating systems, libraries, ApplicationProgramming Interfaces [APIs], applications, applets, etc.), it shouldbe appreciated that embodiments of the invention are not limited to thespecific organization and allocation of program functionality describedherein.

Moreover, various program code described hereinafter may be referred toas being able to align NDE data. However, it should be appreciated thatthe program code is configured to align not only NDE data to a simulatedmodel, but is also configured to align indications of potential problemsto a simulated model, align first NDE data to second NDE data, alignadditional information to a simulated model and/or align other data. Itshould therefore be appreciated that embodiments of the invention arenot limited to the alignment of NDE data described herein.

Those skilled in the art will recognize that the environmentsillustrated in FIGS. 1-4 are not intended to limit the embodiments ofthe invention. Indeed, those skilled in the art will recognize thatother alternative hardware and/or software environments may be usedwithout departing from the scope of the invention.

Software Description and Flows

FIG. 5 is a flowchart 100 illustrating a sequence of operations that maybe performed by the computer 10 of FIG. 1 and/or the computing system 40of FIG. 2 to configure management of NDE data consistent withembodiments of the invention. Returning to FIG. 5, golden NDE dataand/or a simulated model associated with at least a portion of an assetmay be received (block 102). In various embodiments, golden NDE data maybe blueprints, schematics, or other technical information about at leasta portion of an asset. As such, and when golden NDE data is received, asimulated model of at least a portion of an asset may be generated fromgolden NDE data (block 104). In various embodiments, a simulated modelincludes a one-dimensional waveform, a two-dimensional image, and/or athree-dimensional representation of at least a portion of the asset. Inspecific embodiments, the two-dimensional and/or a three-dimensionalsimulated model is a computer aided design (CAD) and/or computer aideddesign and drafting (CADD) model generated through a respective CADand/or CADD program, although different design software may be usedwithout departing from the scope of the invention. NDE data and/orgolden NDE data associated with a portion of an asset may be alignedwith a simulated model associated with the same portion of the asset.The NDE data may then be inspected to determine if there are potentialproblems with that NDE data. For example, potential problems from atleast one asset of a plurality of assets that is associated with thesame portion of the respective at least one asset may be aligned to asimulated model. As such, trends in potential problems may bedetermined. Similarly, a plurality of datasets of NDE data from the sameportion of the same asset may be aligned to a simulated model todetermine a trend of the potential problems of that portion over time.Thus, potential problems may be displayed on a simulated model of atleast a portion of an asset to determine if that particular portion ofan asset is experiencing excessive potential problems across a pluralityof assets or over time.

In addition to generating the simulated model or in response toreceiving the simulated model, a plurality of features for thatsimulated model may be specified (block 104). These features may be usedto align NDE data to the simulated model. For example, a feature mayinclude a seam, a weld, a location of a bolt, a particular straight,curved, round, and/or other uniquely shaped area of the asset, and/oranother feature that may be used to align the NDE data to the simulatedmodel. The computer 10 of FIG. 1 and/or computing system 40 of FIG. 2may also receive a plurality of alignment algorithms with which to alignNDE data to the simulated model (block 106) as well as a plurality ofauditing algorithms with which to audit an alignment of NDE data to thesimulated model (block 108).

It may be advantageous to use various alignment algorithms to align NDEdata collected from various types of NDE data collection devices, usevarious alignment algorithms to align NDE data associated with variousparts of an asset and/or otherwise have various alignment algorithmswith which to align NDE data. For example, a first alignment algorithmmay be more effective at aligning NDE data collected from an x-raymachine, but that first alignment algorithm may not be as effective ataligning NDE data collected from an ultrasound machine. As such, shotdescriptor data structures may be generated that specify alignmentalgorithms, if any, to align NDE data to the simulated model (block110). In specific embodiments, a shot descriptor configuration tool maybe configured to analyze a simulated model and/or NDE data and determinefrom that analysis, or from manual interaction with that shot descriptorconfiguration tool, the alignment algorithms, if any, to align NDE datato the simulated model, and thus generate the shot descriptor datastructures to align NDE data to the simulated model. A shot descriptordata structure may be associated with each NDE data collection deviceand list, in an order, the preferred alignment algorithms with which toalign NDE data from the respective NDE collection device. Thus, forexample, if a first alignment algorithm to align the NDE data producesan unsatisfactory alignment of that NDE data to the simulated model, asecond alignment algorithm to align the NDE data to the simulated modelmay be used. In addition to specifying at least one alignment algorithm,the shot descriptor data structure may specify at least one auditingalgorithm to audit an alignment of the NDE data to the simulated modelto determine whether that alignment has produced a satisfactoryalignment (block 110). In the event that an alignment algorithm is notspecified to align NDE data to a simulated model, the shot descriptordata structure may indicate that the NDE data is to be manually alignedby a user.

In addition to receiving golden NDE data, simulated models, alignmentalgorithms and auditing algorithms, translation information may bereceived (block 112). For example, if the asset is an airplane a portionof the inspection information associated with NDE data may indicate thatthe NDE data is associated with “wing2,” which in turn may indicate thatthe NDE data is associated with a left wing of the airplane. As such,translation information may be used by a translation component toconvert “wing2” to “LWING,” or another identifier. Also for example, atleast some portion of the NDE data may indicate the NDE data collectiondevice that captured that NDE data. As such, translation information maybe used by the translation component to indicate the type of NDE datacollection device that captured the NDE data. This, in turn, may be usedto determine a shot descriptor data structure associated with that NDEdata.

Finally, a confidence threshold for at least one alignment algorithm maybe received (block 114). In particular, the confidence threshold may beused to audit an alignment of NDE data to a simulated model. When anaudit alignment determines that an alignment of NDE data to a simulatedmodel has not reached a particular confidence threshold, the alignmentmay be discarded and/or adjusted. In one embodiment, when the confidencethreshold of an alignment has not been met, the alignment of NDE data tothe simulated model with a first alignment algorithm is discarded and asecond alignment algorithm with which to align the NDE data to thesimulated model is chosen. In an alternative embodiment, when theconfidence threshold of an alignment has not been met, the parametersfor the alignment algorithm used to align the NDE data to the simulatedmodel is adjusted. In a further alternative embodiment, when theconfidence threshold of an alignment has not been met, a user is giventhe opportunity to manually adjust the alignment of that NDE data to thesimulated model.

FIG. 6 is a flowchart 120 illustrating a sequence of operationsconfigured to be performed by the computer 10 of FIG. 1 and/or thecomputing system 40 of FIG. 2 to manage NDE data consistent withembodiments of the invention. As illustrated in FIG. 6, the sequence ofoperations includes receiving NDE data and/or inspection informationassociated with at least a portion of an asset (block 122). In responseto receiving the NDE data and/or the inspection information, dataassociated with the NDE data and/or the inspection information may betranslated into tagged information (block 124) and a session associatedwith the NDE data may be determined (block 126). In response todetermining the session associated with the NDE data, a simulated modelassociated with the NDE data may be retrieved (block 128). Once thesimulated model has been retrieved, the NDE data may be automaticallyaligned to the simulated model with at least one alignment algorithmspecified by a shot descriptor data structure associated with the NDEdata (block 130). A display representation of the aligned NDE data onthe simulated model may be generated in response to alignment of the NDEdata to the simulated model (block 132).

FIG. 7 is a flowchart 140 illustrating the translation process of block124 in FIG. 6. Referring to FIG. 7, the NDE data and/or the inspectioninformation may be analyzed for translatable information (block 142). Insome embodiments, translatable information includes a location on theasset from which the NDE data was captured, an identification of theasset, an identification of a component of the asset, a history of theasset, a time at which the NDE data was captured, a date at which theNDE data was captured, an identification of an inspection location atwhich the NDE data was captured, an identification of a service locationassociated with the NDE data, an identification of an NDE sessionassociated with the NDE data, an indication associated with the NDEdata, an annotation associated with the NDE data, an identification ofan inspector associated with the NDE data, an identification of an NDEdata collection device associated with the NDE data, an orientationassociated with the NDE data, a unique identification of the NDE data, apixel spacing of the NDE data, a photometric interpretation of the NDEdata, a bit depth of the NDE data, color mapping of the NDE data,intensity settings of the NDE data, a status of the NDE data, anuncertainty of a measurement associated with the NDE data and/orcombinations thereof. This translatable information, in specificembodiments, is determined from data associated with the NDE data and/orthe inspection information. When there is translatable information,information may be translated (block 144) with reference to thetranslation information data structure 74, a translation informationfile 146 separate from the translation information data structure 74(e.g., a translation information file 146 separately supplied by theuser and/or otherwise available to translate information) and/or manualtranslation of the information from the user 148. In response totranslating the information, it may be determined whether to store thetranslation (block 150). When it is determined to store the translation(e.g., for example, the translation was performed with reference tomanual translation from the user 148) (“Yes” branch of decision block150) the translation may be stored (block 152) for future reference.When it is determined not to store the translation (“No” branch ofdecision block 150) and/or after storing the translation (block 152) tagtranslation process may end.

FIG. 8 is a flowchart 170 illustrating in more detail the sessionassociation process of block 126 of FIG. 6. Referring to FIG. 8, asession associated with NDE data may be determined by comparingtranslated tags associated with that NDE data and/or inspectioninformation with sessions in the session association data structure(block 172). The session association data structure (block 76) mayinclude a list of sessions during which NDE data may have been capturedfrom an asset, each of which may include the times and/or dates of thatsession, the serial number of the asset inspected in that session, anidentification of the portions of the asset inspected in that session,an identification of the inspector who captured the NDE data, and/or anidentification of datasets of required and/or optional NDE data that mayhave been captured during that session. Thus, in the event that asession includes the capture of a plurality of datasets of NDE data(e.g., from the same portion of an asset or from various portions of theasset, as well as from various NDE data collection devices) and/or asequence to capture that plurality of datasets of NDE data, each datasetof NDE data captured during that session may be associated with thatsession. As such, it may be determined whether a session associated withthe NDE data exists (block 174). When there is no session associatedwith the NDE data (“No” branch of decision block 174), a new session maybe created and associated with the NDE data (block 176) and dataassociated with the new session may be stored in the session associationdata structure (block 178). When there is a session associated with theNDE data (“Yes” branch of decision block 174), or after storing newsession data in the session association database (block 178), the NDEdata may be associated with the existing session or new session,respectively (block 180).

FIG. 9 is a flowchart 190 illustrating in more detail the alignmentalgorithm selection and automatic alignment process of block 130 in FIG.6. Translated information associated with a dataset of NDE data may beused to determine a particular shot descriptor data structure associatedwith that dataset of NDE data. As such, a shot descriptor data structureassociated with that NDE data may be retrieved (block 192). In responseto retrieving the shot descriptor data structure, it may be determinedwhether an alignment algorithm is specified in that shot descriptor datastructure (block 194). In particular, the shot descriptor data structuremay be accessed and it may be determined whether that shot descriptordata structure includes at least one alignment algorithm for the NDEdata. When the shot descriptor data structure includes at least onealignment algorithm (“Yes” branch of decision block 194), the nextalignment algorithm specified in the shot descriptor data structure maybe selected from the algorithm data structure (e.g., in one example, thefirst alignment algorithm specified in the shot descriptor datastructure) (block 196). In an optional step, an auditing algorithmspecified in the shot descriptor data for auditing an alignment with thealignment algorithm selected in block 196 may also be selected from thealgorithm data structure (block 198).

Upon selecting the alignment algorithm with which to align the NDE datato a simulated model, input parameters for that alignment algorithm maybe built (block 200). Input parameters for various alignment algorithmsmay be determined based on the translated tags associated with the NDEdata (e.g., the type of NDE data collection device used to capture theNDE data) and/or the inspection information (e.g., data concerning theasset, component, angle, orientation, or other information associatedwith the NDE data). The input parameters for a particular dataset of NDEdata may include that the NDE data should be decimated, filtered usingedge detection, rotated, oriented, noise filtered, that seed values beapplied to the NDE data, that the scale of the NDE data should beadjusted (e.g., the NDE data should be enlarged or reduced), thatportions of the NDE data should be deformed (e.g., some portion of theNDE data should include local distortion to account for manufacturingvariances, parallax distortion, and/or other variances), and/or thatother input parameters should be applied to the NDE data for alignmentto the simulated model. Moreover, input parameters may be built at leastpartly based upon input parameters and/or an alignment adjustment of aprevious alignment (e.g., input parameters for a second alignmentalgorithm may be built or otherwise based upon the input parameters fora first alignment algorithm and/or the alignment adjustments to analignment by the first alignment algorithm). The input parameters mayalso include NDE data features and/or a golden NDE data. Using the inputparameters, the NDE data may be automatically aligned to the simulatedmodel with the alignment algorithm (block 202). As illustrated in FIG.9, after the NDE data has been aligned to a simulated model (block 202)the input parameters for that alignment may be fed back to build newinput parameters for a subsequent alignment algorithm as at 203.

In response to aligning the NDE data to the simulated model, thealignment may be audited (block 204). In particular, auditing thealignment may include determining a confidence of the alignment of theNDE data to the simulated model and comparing that confidence to aconfidence threshold associated with the alignment algorithm andspecified by a shot descriptor data structure associated with that NDEdata. When the confidence of the alignment of the NDE data to thesimulated model is unsatisfactory and/or when the confidence thresholdassociated with the alignment algorithm has not been met or exceeded(“No” branch of block 206) it may be determined whether anotheralignment algorithm in the shot descriptor data structure associatedwith the NDE data has been specified (block 208) and the sequence ofoperations may return to block 194.

Returning to block 194, when an alignment algorithm is not specified ina shot descriptor data structure associated with the NDE data, when theconfidence of a an alignment is unsatisfactory and there is no otheralignment algorithm specified in the shot descriptor data structureassociated with the NDE data, and/or when the confidence threshold of analignment has not been met and there is no other alignment algorithmspecified in the shot descriptor data structure associated with the NDEdata (“No” branch of decision block 194), a user interface may beprovided to a user for manual alignment of the NDE data to the simulatedmodel (block 210). That user interface may receive a manual alignment ofthe NDE data to the simulated model (block 211). When the confidence ofan alignment of NDE data to a simulated model is satisfactory, when theconfidence threshold of an alignment algorithm has been met or exceeded(“Yes” branch of block 208), and/or after the manual alignment of NDEdata to the simulated model (block 210), a display representation of thealigned NDE data to the simulated model may be generated (block 212)and/or a report associated with the aligned NDE data, and thus at leasta portion of the asset, may be generated (block 214). In an optionalstep and in response to generating a display representation of thealigned NDE data to the simulated model (block 212), and in particularafter an automatic alignment of the NDE data to the simulated model(block 202), a user interface may be provided to a user for anadjustment of the alignment (block 216), and that user interface mayreceive an adjustment of the alignment of the NDE data to the simulatedmodel (block 217). When the user tunes an automatic alignment of the NDEdata to the simulated model (“Yes” branch of decision block 218) theadjustment of the alignment may be stored in an alignment adjustmentdata structure associated with that alignment algorithm (block 220).When the user does not tune an automatic alignment of the NDE data tothe simulated model (“No” branch of decision block 218) the alignmentparameters of the alignment may be stored (block 222) and the alignmentof the NDE data to the simulated model may end.

FIG. 10 is a flowchart 230 illustrating detection of missed coverage ofat least a portion of a simulated model consistent with embodiments ofthe invention. In some embodiments, the missed coverage is indicatedidentifying those portions of the simulated model which are notassociated with a dataset of NDE data and generating a display of thoseportions. Thus, NDE data may be automatically aligned to a simulatedmodel associated with that NDE data (block 232) and at least a portionof the alignment without aligned NDE data may be identified (block 234).In a specific example, the portion of the alignment without aligned NDEdata are identified by changing a color of the background of the displayrepresentation upon which aligned NDE data and the simulated model aredisplayed. In this manner, a user may simply look for those parts of thealignment which have the color to determine those parts of the simulatedmodel that are not associated with NDE data. In an optional step, atleast a portion of the simulated model that are not associated withaligned NDE data may be automatically identified (block 236). Inspecific embodiments, the portion of the simulated model may bedetermined through a filler algorithm configured to identify a part ofthe simulated model that are not associated with aligned NDE data, thenassociate that part with a color. In some embodiments, the identified atleast a portion of the alignment without the aligned NDE data and the atleast a portion of the simulated model without aligned NDE data may belogically AND'd (block 238) and a display representation of the at leasta portion of the alignment without aligned NDE data and/or the logicallyAND'd portions may be generated (block 240). As such, a user may be ableto view portions of a simulated model, or portions of an alignment, thatare not associated with aligned NDE data. Thus, the user may be able todetermine whether the coverage of at least a portion of an assetassociated with that simulated model was acceptable, in that thecoverage may be unacceptable when there is at least a portion of thesimulated model that is not associated with aligned NDE data.

FIG. 11 is a flowchart 250 illustrating a sequence of operations toalign two datasets of NDE data with each other and determine thelocation of an indication of a potential problem. In particular a firstdataset of NDE data that may include an indication of a potentialproblem may be identified (block 252). For example, the first NDE datamay be an x-ray image of at least a portion of an asset. A seconddataset of NDE data associated with the first NDE data and that isfurther associated with an indication of a potential problem may also beidentified (block 254). For example, the second NDE data may be a CTslice of the at least a portion of the asset. Moreover, the second NDEdata may be associated with an indication that may not be in turnassociated with the first NDE data. As such, a plurality of projectionsof the second NDE data may be generated through the first NDE data(block 256) and a projection from among the plurality of projectionsthat results in an acceptable alignment (e.g., a projection of thesecond NDE data that substantially matches the first NDE data at and/oraround the location of the indication) may be selected (block 258). Forexample, a plurality of projections of the CT slice may be generatedthrough the x-ray image, and the projection with the most minimaldifference, or smallest error, between the slice and image may beselected.

In response to selecting the projection, the second NDE data may beautomatically aligned to first NDE data and/or the first and second NDEdata may be aligned to a simulated model associated with both the firstand second NDE data consistent with embodiments of the invention (block260). For example, the x-ray image and CT slice may be of a fan blade ofa turbine engine, and the x-ray image and/or CT slice may be aligned tothemselves and/or a simulated model of the fan blade of the turbineengine. In response to automatically aligning the first and/or secondNDE data, the location of the indication associated with the first NDEdata may be associated with a corresponding location on the first NDEdata and/or the simulated model (block 262) and a display representationof the corresponding location of the indication on the first NDE dataand/or the simulated model may be generated (block 264). Thus, forexample, the location of an anomaly of the CT slice may be associatedwith a location on the x-ray image. By aligning both the CT slice to thex-ray image, the location of the anomaly may be located on the x-rayimage. Similarly, by aligning both the x-ray image and the CT slice tothe simulated model, the location of the anomaly may be located withinthe three dimensional space of the simulated model.

FIG. 12 is a flowchart 270 illustrating a sequence of operations tomaintain data integrity of NDE data captured from an NDE data collectiondevice consistent with embodiments of the invention. Over time, NDE datacollection devices may experience wear and otherwise experiencemalfunctions that are evident in the NDE data that they capture. Inparticular, NDE data collection devices that utilize computedradiography are typically configured with phosphor plates instead offilm. These phosphor plates are often prone to wear from the absorptionof radiation over time as well as to rough handling often associatedwith industrial settings. In some embodiments of the invention, bytrending NDE data collected from an NDE data collection device thatutilizes computed radiography and a phosphor plate, the wear to thatphosphor plate can be identified. Thus, a plurality of datasets of NDEdata associated with at least a portion of an asset, and captured with afirst NDE data collection device, may be retrieved (block 272) andautomatically aligned to a simulated model associated with thatplurality of datasets of NDE data (block 274). The plurality ofalignments may be analyzed to determine a trend in the intensity ofthose NDE datasets over time, noise in NDE datasets, a commondisturbance in NDE datasets and/or a trend of that disturbance overtime, as well as other variations in the plurality of datasets of NDEwith respect to time (block 276).

The intensity, noise, disturbances and/or other variations in theplurality of datasets of NDE data may be compared to some metric ofexpected NDE data collected with the first NDE data collection device todetermine a deviation of the plurality of datasets of NDE data (block278). When there is no deviation in the plurality of NDE datasets (“No”branch of decision block 280) the sequence of operations 270 may end(block 282). When there is a deviation in the plurality of NDE datasets(“Yes” branch of decision block 282), the deviation, and thus apotential variance of the first NDE data collection device, may beindicated (block 284). This, in turn, may be transferred to an inspectorthat utilizes the NDE data collection device and thus informs thatinspector of wear of at least a portion of the NDE data collectiondevice.

FIG. 13 is a flowchart 290 illustrating a sequence of operations toretrieve NDE data associated with an indication from among a pluralityof indications consistent with embodiments of the invention. Inparticular, the sequence of operations may be used when a plurality ofindications associated with a plurality of datasets of NDE data arealigned and displayed on a simulated model associated with the pluralityof datasets of NDE data. In response to a user selecting a particularindication, the NDE data associated with that particular indication maybe retrieved. Thus, the plurality of datasets of NDE data associatedwith at least a portion of an asset may be retrieved (block 292). Inresponse, the inspection information associated with each of therespective plurality of datasets of NDE data may also be retrieved(block 294). In response to retrieving the inspection information, atleast one indication associated with inspection information isautomatically aligned to a simulated model associated with the pluralityof datasets of NDE data (block 296). A display representation of the atleast one indication on the simulated model is then generated (block298). In response to user interaction with the at least one indication(e.g., a user clicking, tabbing to and/or otherwise selecting the atleast one indication), each dataset of NDE data from among the pluralityof datasets of NDE data associated with the at least one indication areretrieved and automatically aligned with the simulated model, and thedisplay representation is amended to display the aligned NDE data andthe at least one indication on the simulated model (block 300).

FIG. 14 is a flowchart 310 illustrating a sequence of operations tofilter indications consistent with embodiments of the invention.Initially, a plurality of datasets of NDE data associated with at leasta portion of an asset are retrieved (block 312). Similarly, inspectioninformation associated with each of the respective plurality of datasetsof NDE data may be retrieved (block 314). The plurality of datasets ofNDE data and respective inspection information may be retrieved inresponse to user interaction to specify the at least a portion of theasset. Indications of potential problems associated with the pluralityof datasets of NDE data may be automatically aligned to a simulatedmodel associated with the plurality of datasets of NDE data (block 316).In turn, a display representation of the indications on the simulatedmodel may be generated, along with selectable filtering parameters forthe indications (block 318). For example, the selectable filteringparameters may be interacted with to select the type of indications todisplay (e.g., cracks, abrasions, etc.), more specific portions of theat least a portion of the asset, indications associated with aparticular NDE data collection device, a time (e.g., an hour, a day, aweek, a month, a year and/or a range thereof) associated with thatindication, and/or indications to display based upon another selectablefiltering parameter.

In response to user interaction with the display representation ofselectable filtering parameters, a filter to apply to the indications toselectively filter at least one indication is determined (block 320) andapplied to the plurality of indications to remove that at least oneindication from the aligned plurality of indications (block 322). Inturn, the display representation is amended to display those indicationsthat have not been removed (e.g., the “filtered indications), and theselectable filtering parameters for the filtered indications are updated(block 324).

FIG. 15 is a flowchart 330 illustrating a sequence of operations toresample NDE data and produce a plurality of resolutions of that NDEdata for subsequent display consistent with embodiments of theinvention. For example, a dataset of NDE data may approach severalhundred megabytes in size. This, in turn, may be difficult to display.As such, the NDE data may be resampled using bilinear interpolation togenerate a plurality of resamples of NDE data from a dataset of NDEdata. In particular, a resample of NDE data may be smaller than the NDEdata and thus require less resources to view. As such, a first resampleof NDE data that is smaller in size than the NDE data may be viewed at alower magnification of view of an alignment when it is unnecessary toillustrate all details of the NDE data, while a second resample of NDEdata that is larger in size than the first resample of NDE data may beviewed at a higher magnification of view of the alignment to illustratedfiner details. Thus, NDE data may be loaded (block 332) and that NDEdata may be sampled using bilinear interpolation into a plurality ofresampled sets of NDE data (block 334). For example, the NDE data may bedecimated or otherwise sampled to produce resampled sets of NDE data.More specifically, the NDE data may be an image and the plurality ofresampled sets of the NDE data may include a resampled set of NDE dataat one-half the resolution of the NDE data, a resampled set of NDE dataat one-fourth the resolution of the NDE data, and so on. It will beappreciated that any fraction of the NDE data may be specified for aresampled set of NDE data, including three-fourths, seven-eighths,one-tenth, one-thousandth, etc. Upon generating the plurality ofresampled sets of NDE data, the NDE data as well as the plurality ofresampled sets of NDE data may be stored (block 336).

FIG. 16 is a flowchart 340 illustrating a sequence of operations todisplay a dataset of NDE data or a resampled set thereof consistent withembodiments of the invention. In response to a request for the NDE data(e.g., in response to a request from a user to view a portion of anasset associated with the NDE data, or otherwise align the NDE data),the characteristics of a display representation associated with thatrequest (e.g., a display representation that includes the NDE data) aredetermined (block 342). In particular, the display representationcharacteristics may include the current zoom level at which to displaythe NDE data. Thus, a first resample of NDE data from among a pluralityof resampled sets of NDE data may be selected based upon a size of thefirst resample of NDE data and/or an optimal resolution for the displayrepresentation (block 344). In an optional step, the first resample ofNDE data may then be sampled using nearest neighbor interpolation (block345). For example, if it is determined that the optimal resolution forthe display representation should be a resampled set of NDE data that isone-third the resolution of the original NDE data, the sequence ofoperations may select the resampled set of NDE data that is one-half theresolution of the original NDE data (e.g., the resampled set of NDE datathat is closest to the desired resolution and that is also larger thanthe desired resolution for more accurate re-sampling) and sample thatNDE data to one-third the resolution of the original NDE data. Afterselecting the first resample of NDE data, or after re-sampling the firstresample of NDE data, the first resample is automatically aligned to asimulated model associated with that first resample of NDE data (block346) and a display representation of the aligned first resample of NDEdata on the simulated model is generated (block 348).

FIG. 17 is a flowchart 350 illustrating a sequence of operations toassign a location descriptor to at least a portion of an asset andtranslate location information associated with NDE data (e.g., locationinformation specifying the location of an indication, asset, portion ofan asset, sub-portion of an asset, etc.) consistent with embodiments ofthe invention. Initially, a plurality of locations associated with asimulated model, and in turn associated with at least a portion of anasset, are identified (block 352). For example, one location mayidentify that the simulated model is associated with the left wing of anasset, a second location may identify a portion of the simulated modelthat is associated with a slat of the left wing, a third location mayidentify a portion of the simulated model that is associated with aspoiler of the left wing, a fourth location may identify that a portionof the simulated model is associated with a bolt of the left wing, theslat or the spoiler, etc. A plurality of location descriptors may thenbe assigned to at least some of the respective identified locations(block 354). For example, a location descriptor may describe the leftwing as “WING2,” the slat as “SLAT1,” the spoiler as “SPOIL1,” and thebolt as “BOLT289.”

In response to retrieving NDE data and/or inspection informationassociated therewith (block 356), location information associated withthe NDE data and/or the inspection information may be determined (358).In particular, the determined location information may include locationinformation specifying the at least a portion of the asset associatedwith the NDE data and/or the inspection information. Additionally and/oralternatively, the determined location information may include alocation information specifying the location of an indication of apotential problem in turn associated with the NDE data and/or theinspection information. In response to determining the locationinformation, a first location among the plurality of locationsassociated with the location information may be determined (block 360)and a respective first location descriptor among the plurality oflocation descriptors associated with the first location may be assignedto the determined location information (block 362). Thus, a locationassociated with the NDE data and/or the location of an indication may beautomatically determined and assigned to that NDE data.

FIG. 18 is a flowchart 370 illustrating in more detail the translationprocess 144 of FIG. 7. In response to determining that there istranslatable information to translate, a text string of the translatableinformation is retrieved and parsed (block 372). In some embodiments,the text string is parsed by separating each element of the text string(e.g., each element of the text string separated by at least one spacecharacter). In this manner, the first element of the text string isselected (block 374) and a lexicon or other semantic concept in thetranslation information data structure and/or translate file is searchedfor that first element (block 376). When there is a match for theelement (“Yes” branch of decision block 378) that element isautomatically translated to the standardized element (block 380). Whenthere is not a match (“No” branch of decision block 378) a userinterface may be provided to translate the element to a standardizedelement (block 382) and the element may be translated to thestandardized element manually (block 380).

In response to translating the element either manually or automatically,the most recently standardized element and at least one previouslystandardized element may be analyzed for an updated meaning (block 384).In particular, the most recently standardized element and at least onepreviously standardized element may be compared to the semantic conceptsto determine if their meaning should be updated. The sequence ofoperations may then determine whether the end of the string has beenreached (block 386). When the end of the string has been reached (“Yes”branch of decision block 386) a new text string with at least onestandardized element is output (block 388). In particular, the new textstring may be output and stored in inspection information associatedwith NDE data. When the end of the string has not been reached (“No”branch of decision block 386) the next element of the text string isselected (block 390) and the sequence of operations returns to block376. It will be appreciated that the flowchart 370 may be repeated forat least a portion of translatable information associated with NDE dataand/or inspection data associated therewith consistent with embodimentsof the invention.

FIG. 19 is a flowchart 400 illustrating a sequence of operations todisplay NDE data of a first modality as NDE data of a second modalityconsistent with embodiments of the invention. In particular, it may beuseful to display the NDE data of the first modality as NDE data of thesecond modality for ease of users by users familiar and trained tointerpret NDE data of the second modality. Thus, NDE data and/orinspection information associated therewith is retrieved (block 402) andthe modality of the NDE data (e.g., the “first” modality) is determined(block 404). In an optional step, the NDE data may be automaticallyaligned to a simulated model associated therewith (block 406). Using amodel of the NDE data collection device that collected the NDE data, theknown structure of the at least a portion of the asset with which theNDE data is associated and/or data from the inspection information, amissing dimensionality of the NDE data may be determined (block 408). Amodel of a second modality to simulate the NDE data as NDE data of thesecond modality may be used (block 410) and a display representation ofthe aligned NDE data on the simulated model as NDE data of the secondmodality may be generated (block 412).

FIG. 20 is a flowchart 420 illustrating a sequence of operations todisplay indications from a plurality of datasets of NDE data associatedwith a similar portion of a plurality of assets consistent withembodiments of the invention. For example, user input specifying a typeof asset is received (block 422) and user input specifying at least aportion of that type of asset is also received (block 424). In responseto the specification of at least a portion of the type of asset, aplurality of datasets of NDE data and/or respective inspectioninformation associated with the at least a portion of the type of assetmay be retrived (block 426) and indications associated with theplurality of datasets of NDE data and/or respective inspectioninformation may be automatically aligned to a simulated model associatedwith the at least a portion of the type of asset (block 428). As such, adisplay representation of the indications associated with the pluralityof datasets of NDE data and/or the respective inspection information onthe simulated model is generated (block 430).

For example, a user may wish to view indications associated with apopulation of a particular type of asset. The asset may be a tank, andthe user may wish to view indications for all right front turret armorsections of a plurality of tanks. Thus, the user may specify a tank asthe type of asset and the portion of the tanks (e.g., right front turretarmor sections) that they wish to view indications for. The datasets ofNDE data and inspection information associated with the right frontturret armor sections of a plurality of tanks may be retrieved, and anyindications therein automatically aligned to a simulated model of theright front turret armor sections of a tank. Thus, a displayrepresentation of the indications associated with the respective rightfront turret armor sections of a plurality of tanks may be displayed forthe user to view potential population-wide problems, and any trends forindications of that particular population may be determined.

FIG. 21 is a flowchart 440 illustrating a sequence of operations todisplay indications from a plurality of datasets of NDE data associatedwith at least a portion of a particular asset consistent withembodiments of the invention. For example, user input specifying anasset, and specifically a particular asset, is received (block 442) anduser input specifying at least a portion of the particular asset is alsoreceived (block 444). In some embodiments, the particular asset may beidentified by a unique identification, such as a serial number of thatparticular asset. In response to the user input, a plurality of datasetsof NDE data and/or respective inspection information associated with theat least a portion of the particular asset are retrieved (block 448) anda display representation of the indications associated with theplurality of datasets of NDE data and/or the respective inspectioninformation on the simulated model is generated (block 450).

For example, a user may wish to view indications associated with aparticular asset. The asset may be a particular tank, and the user maywish to view indications for the right front turret armor section ofthat tank. Thus, the user may specify the tank by serial number and theportion of the tank (e.g., right front turret armor section) that theywish to view indications for. The datasets of NDE data and inspectioninformation associated with the right front turret armor section of theparticular tank may be retrieved, and any indications thereinautomatically aligned to a simulated model of the right front turretarmor section of that type of tank. Thus, a display representation ofthe indications associated with the respective right front turret armorsection of a particular tank may be displayed for the user to viewpotential problems of that tank, and any trends in indications for thatparticular tank may be determined.

FIG. 22 is a flowchart 460 illustrating a sequence of operations todetermine uncertainty of a location on a simulated model consistent withembodiments of the invention. In particular, the sequence of operations460 may be used to specify a range of uncertainty of a location, such asthe location of an indication. As such, at least one range ofuncertainty may be specified (block 462) and that at least one range ofuncertainty may be associated with at least one location on a simulatedmodel (block 464). For example, the range of uncertainty for aparticular location may be associated with a portion of an asset or asub-portion thereof. More specifically, and also for uncertainty, therange of uncertainty for an axle beam of a landing gear on an aircraftmay include a first range for the axle beam itself, a second range forthe axle beam and couplers of the axle beam to a tire, or the entireaxle beam assembly in which that axle beam is disposed. Alternatively,the range of uncertainty may be specified as a probability distributionfunction such that the further from the center of the location, thesmaller the probability of the location being encompassed in theprobability distribution function. Alternatively, the uncertainty maynot be specified, and instead may be determined from at least one of theinspection information associated with NDE data, the modality of NDEdata, and/or the alignment of the NDE data to the simulated model.

In response to associating the at least one range of uncertainty with atleast one location on a simulated model, a location descriptor isassigned to the respective at least one location and/or a locationdescriptor previously associated with the respective at least onelocation is determined (block 466). Thus, and in some embodiments,various ranges of uncertainty may be associated with various locations,and thus various location descriptors, on a simulated model. Also inthis manner, an indication specifying a location on the simulated modelmay be automatically associated with not only a location descriptor butalso a range of uncertainty. For example, location informationassociated with at least a portion of NDE data and/or locationinformation may be determined (block 468). In particular, the locationinformation may be determined in response to receiving the NDE dataand/or location information. In response to determining the locationinformation, a location associated with the location information may bedetermined (block 472) and, in response to determining the location, alocation descriptor associated with the location may be determined(block 472). As such, the NDE data may be automatically aligned to asimulated model associated with the NDE data (block 474) and a displayrepresentation of the aligned NDE data on the simulated model thatincludes the at least one range of uncertainty of the location may begenerated (block 476).

FIG. 23 is a flowchart 480 illustrating a sequence of operations todetermine uncertainty of a location on a simulated model due to theinspection process, and particular due to the various modalitiesassociated with NDE data, consistent with embodiments of the invention.In particular, it may be useful to display a range of uncertaintyassociated with a particular modality of NDE data, or otherwise displaya range of uncertainty associated with the capture of NDE data with anNDE data collection device. Thus, NDE data and/or inspection informationassociated therewith is retrieved (block 482) and the modality of theNDE data is determined (block 484). Thereafter, an uncertainty (e.g., arange of uncertainty) in the NDE data due to the modality of the NDEdata may be determined (block 486) and an estimated distortion of atleast a portion of the NDE data due to the process to capture the NDEdata may be determined (block 488). In some embodiments, the uncertaintyin the NDE data due to the modality of the NDE data is specified by auser. Alternatively, the uncertainty in the NDE data due to the modalityof the NDE data may be retrieved from a previous specification by theuser. The distortion, however, may be determined by how NDE data iscaptured. For example, NDE data may be two-dimensional NDE data capturedfrom a three-dimensional portion of an asset. Thus, there is distortionbetween the two-dimensional NDE data and the simulated model associatedtherewith. Thus, the distortion and/or the uncertainty may be used togenerate a range of uncertainty and/or an uncertainty probabilitydistribution function associated with that NDE data (block 490).

After determining the range of uncertainty and/or the uncertaintyprobability distribution function associated with NDE data, anindication of a potential problem associated with the NDE data may beautomatically aligned to a simulated model associated with the NDE data(block 492) and the center of the range of uncertainty and/or theuncertainty probability distribution function may be automaticallyaligned to the location of the indication (block 494). Thus, thelocation of the indication as well as the range of uncertainty and/orthe uncertainty probability distribution function associated with thelocation of the indication may be generated on a display representation(496). In this manner, the uncertainty in the location of an indicationdue to the capture of the NDE data with the NDE data collection devicemay be determined and displayed.

FIG. 24 is a flowchart 500 illustrating a sequence of operations todisplay a plurality of indications of portions of at least one assetthat are substantially structurally similar, but that may be located atdifferent locations on the at least one asset or that are oriented indifferent manners respective to each other. For example, the sequence ofoperations 500 may be used to display a first and second indicationassociated with a respective first and second wing (e.g., of the same ordifferent assets) on a simulated model of the first wing by transformingthe location of the second indication on the second wing into a locationon the first wing. Thus, first NDE data is automatically aligned to afirst simulated model associated with the first NDE data (block 502) anda first indication associated with the first NDE data is automaticallyaligned to a first location on the first simulated model (block 504).Similarly, second NDE data is automatically aligned to a secondsimulated model associated with the second NDE data (block 506) and asecond indication associated with the second NDE data is automaticallyaligned to a second location on the second simulated model (block 508).The second location of the second indication on the second simulatedmodel is then transformed to a corresponding third location on the firstsimulated model (block 510). For example, when the first NDE data is NDEdata from a right wing of an asset and the second NDE data is NDE datafrom a left wing of the asset, the transformation between the simulatedmodels associated with the respective first and second NDE data mayinclude a mirror reflection transformation between the simulated modelsassociated with the respective first and second NDE data. Also forexample, the transformation may include transforming a location acrossat least one axis of transformation between the simulated modelassociated with the respective first and second NDE data. A displayrepresentation of the first indication at the first location on thefirst simulated model, as well as the second indication at the thirdlocation on the first simulated model, is then generated (block 512). Inthis manner, indications of portions of at least one asset that aresubstantially structurally similar but otherwise oriented in differentmanner respective to each other may be viewed on a single simulatedmodel. Thus, a user may view trends for at least two different portionsof at least one asset on one simulated model.

FIG. 25 is a flowchart 520 illustrating a sequence of operations tolocally deform at least a portion of the NDE data. Initially, NDE datamay be automatically aligned to a simulated model associated with theNDE data with an alignment algorithm that rigidly attempts to align theNDE data with the simulated model (e.g., a “rigid” alignment algorithmthat only rotates, translates and/or scales NDE data) (block 522). Forexample, and not intending to be limiting, a rigid alignment algorithmmay attempt to align the NDE data to the simulated model based onmatching at least one straight-line section of the NDE data to at leastone corresponding straight-line section on the simulated modelregardless of other portions of the NDE data. However, the alignment ofa portion of the NDE data may be slightly distorted in relation to thesimulated model and/or the asset. In particular, the NDE data may beslightly distorted due to the manner in which that NDE data is capturedfrom the asset, human error and/or the NDE data collection device usedto capture the NDE data, for a few examples. As such, local distortionof the aligned NDE data may be identified (e.g., the local distortionbetween at least one and/or all portions of the NDE data that were notused to align the NDE data) (block 524) and it may be determined whetherthat local distortion exceeds a distortion threshold for distortion(block 526). When the local distortion (e.g., the local distortion of atleast one and/or all portions of the NDE data that were not used toalign the NDE data) does not exceed a distortion threshold (“No” branchof decision block 526) no action may be taken (block 528). However, whenthe local distortion exceeds the distortion threshold (“Yes” branch ofdecision block 528), the local distortion may be analyzed to determineif that local distortion is due to the modality of the NDE data (block530). When the local distortion is due to the modality of the NDE data(“Yes” branch of decision block 530), the local distortion may beanalyzed to determine if the local distortion is correctable through theuse of a model of the NDE data collection device used to capture the NDEdata (block 532). When the local distortion is not correctable throughthe use of the model of the NDE data collection device used to capturethe NDE data (“No” branch of decision block 532), the NDE data may beautomatically re-aligned to the simulated model using an alignmentalgorithm that does not rigidly attempt to align the NDE data with thesimulated model (e.g., a “non-rigid” alignment algorithm) and/or thealigned NDE data may be adjusted with a local deformation algorithm(block 534).

In some embodiments, the non-rigid alignment algorithm attempts to alignat least two portions of NDE data with at least two correspondingportions of a simulated model associated with the NDE data. Thenon-rigid alignment algorithm may include at least one local deformationalgorithm that may be used when a first portion of the NDE data alignswith the simulated model but a second portion does not. Thus, thealignment algorithm may utilize the local deformation algorithm on thesecond portion and/or the first portion in an attempt to align the NDEdata to the simulated model. In alternative embodiments, a localdeformation algorithm is utilized to align at least a portion of alignedNDE data to the simulated model to reduce local distortion. As such, thelocal deformation algorithm may be applied to at least one portion ofthe NDE data to more closely align that at least one portion to thesimulated model. Returning to block 530, when the local distortion isnot due to the modality of the NDE data (“No” branch of decision block530), the NDE data may be automatically re-aligned to the simulatedmodel using the non-rigid alignment algorithm and/or the aligned NDEdata may be adjusted with a local deformation algorithm (block 534). Insome embodiments, the non-rigid alignment algorithm is utilized tore-align the NDE data to the simulated model (block 534) when thealignment algorithm used in block 522 is a rigid alignment algorithm. Incorresponding embodiments, the local deformation algorithm is used toadjust the aligned NDE data (block 534) when the alignment algorithmused in block 522 is a non-rigid alignment algorithm.

Returning to block 532, when the local distortion is correctable throughthe use of the NDE data collection device model (“Yes” branch ofdecision block 532), the model of the NDE data collection device and atleast one structure of the NDE data and simulated model (e.g., at leastone structure of the at least a portion of the asset that is associatedwith both the NDE data and the simulated model) may be used to adjustthe local distortion of the NDE data (block 536). In some embodiments,the model of the NDE data collection device includes at least oneparameter to adjust NDE data associated with the respective modality ofNDE data captured with that NDE data collection device. Thus, thealignment of a portion of the NDE data to the simulated model may beadjusted using the NDE data collection device model and at least oneknown structure common to the NDE data and the simulated model tocorrect local distortion.

In response to automatically re-aligning the NDE data to the simulatedmodel using a non-rigid alignment algorithm (block 534), adjusting thealigned NDE data with a local deformation algorithm (block 534), orusing the NDE data collection device model and at least one knownstructure of the NDE data and the simulated model to adjust localdistortion (block 536), the alignment of the NDE data to the simulatedmodel may be analyzed to determine if the alignment is parameterizable(e.g., whether the alignment of the NDE data to the simulated model canbe expressed through at least one parameter to align subsequent NDE datato the simulated model) (block 538). When the alignment of the NDE datato the simulated model is parameterizable (“Yes” branch of decisionblock 538) the alignment parameters for that alignment of the NDE datato the simulated model are stored (block 540). When the alignment of theNDE data to the simulated model is not parameterizable (e.g., such aswhen a portion of the NDE data is adjusted in blocks 534 or 536) (“No”branch of decision block 538) the adjusted NDE data itself, as well asthe alignment of that adjusted NDE data, are stored (block 542).

Further details and embodiments of the present invention will bedescribed by way of the following examples.

Example 1

By way of example, FIG. 26 illustrates a display representation of atleast a portion of a dataset of NDE data 600 that may be capturedconsistent with embodiments of the invention. In particular, the NDEdata 600 of FIG. 26 may be captured from at least a portion of an asset,and for this example may be captured from at least a portion of a rightwing of an aircraft. FIG. 27 illustrates a display representation of asimulated model 610 of that right wing of the aircraft. Consistent withembodiments of the invention, it may be advantageous to align the NDEdata 600 on the simulated model 610. As such, an alignment algorithm toalign the NDE data 600 to the simulated model 610 may be determined fromthe NDE data 600 and/or inspection information associated therewith. TheNDE data 600 may then be automatically aligned to the simulated model610 with that alignment algorithm. FIG. 28 illustrates a displayrepresentation of aligned NDE data 620, and in particular illustratesthe NDE data 600 of FIG. 26 rotated to align with the simulated model610 of FIG. 27. FIG. 29 is a display representation 630 that visuallyrepresents the aligned NDE data 620 of FIG. 28 on the simulated model610 of FIG. 27.

It will be appreciated by one having skill in the art that a pluralityof datasets of NDE data may be automatically aligned with the simulatedmodel 610 without departing from the scope of the invention.Subsequently, a display representation of the plurality of aligneddatasets of NDE data on the simulated model may be generated. Thus,first and second NDE data may be received, a first alignment algorithmto align the first and second NDE data to the simulated model may bedetermined, and the first and second NDE data may be automaticallyaligned to the simulated model. A display representation of thesimulated model with the first and second NDE data may be generated. Insome embodiments, a second alignment algorithm to align the second NDEdata may be determined and the second NDE data may be automaticallyaligned to the simulated model. In those embodiments, the displayrepresentation of the simulated model with the first and second NDE datamay still be generated.

Example 2

Due to the capture and transformation of NDE data, the exact location ofan indication may be unknown, and the approximate location of anindication may be represented. By way of example, FIG. 30 illustrates adisplay representation 640 that illustrates uncertainty of a determinedlocation for an indication on a simulated model 610. The displayrepresentation 640 visually represents two indications as at 642 and644, and more specifically illustrates a probability density as at 642and 644 of a distance from the determined location for the indication.Simply put, the closer to the center of the probability density 642and/or 644 (e.g., which may be a Gaussian distribution density), themore likely that is the actual location of the indication. For example,NDE data and/or inspection information may indicate the location of anindication. The location of the indication may thus be indicated as apoint on the simulated model 610 and/or a probability density 642, 644at the point. In this manner, relative locations of indications may bedetermined.

Similarly, and by way of an alternative example, FIG. 31 illustrates adisplay representation 650 that illustrates an uncertainty of adetermined location for an indication on a simulated model 610. Thedisplay representation 650 visually represents an indication as at 652,and more specifically illustrates that at least a portion 654 of thesimulated model 610 may be highlighted to indicate that the portion 654is associated with an uncertainty of the exact location for theindication 652. Specifically, a color component of at least a portion654 of the simulated model 610 may be adjusted to indicate that theportion 654 is associated with an uncertainty of the exact location forthe indication 652. For example, NDE data and/or inspection informationmay indicate the location of an indication. The location of theindication may thus be indicated as a point 652 on the simulated model610 and/or as a portion 654 of the simulated model 610.

Example 3

During inspection of an asset, at least a portion of NDE data for theasset may not be captured, leading to lapses in coverage. For example,an inspection may involve capturing a plurality of datasets of NDE datafrom a plurality of portions of an asset. In some embodiments, theinspection involves capturing NDE data from overlapping portions of anasset. However, when a portion of the asset is not captured, there is alapse of coverage of the asset. As such, embodiments of the inventionare configured to indicate that at least a portion of the simulatedmodel is not aligned with NDE data. By way of example, FIG. 32illustrates a display representation 660 of the simulated model 610 inwhich at least a portion of the display representation 662, andspecifically at least a portion of the background thereof, has beenhighlighted to indicate a lapse in coverage of aligned NDE data with thesimulated model 610. In specific embodiments, the color component of theportion of the display representation 662 that aligned NDE data on thesimulated model 610 is supposed to cover, but that is otherwise notcovered, may be adjusted. In this manner, the lapse in coverage may beeasily located. By way of alternative example, FIG. 33 illustrates adisplay representation 670 in which at least a portion 672 of thesimulated model 610 has been highlighted to indicate a lapse in coverageof aligned NDE data with the simulated model 610. In specificembodiments, the color component of the portion 672 of the simulatedmodel 610 may be adjusted.

Example 4

In some embodiments, it may be advantageous to illustrate a plurality ofindications of a plurality of portions of the same type of asset, suchas indicate a plurality of indications of a right wing of a fleet ofsimilar aircraft. As such, embodiments of the invention are configuredto determine at least one indication, and a location thereof, associatedwith NDE data and/or inspection information from the plurality ofportions of the same type of asset. Embodiments of the invention arefurther configured to visually represent that at least one indication onthe NDE data of the plurality of portions of the same type of assetand/or visually represent that at least one indication on a simulatedmodel associated with the plurality of portions of the same type ofasset. Indications may be searched and/or selectively filtered and adisplay representation may visually represent the searched and/orselectively filtered indications. By way of example, FIG. 34 illustratesa display representation 680 of the simulated model 610 in which aplurality of indications have been visually represented. The pluralityof indications may be from a plurality of portions of the same type ofasset, and thus illustrated on the simulated model 610 associated withthat portion of the same type of asset. Specifically, the displayrepresentation 680 illustrates a search and filter component 682(hereinafter, “search/filer” component 682) and a selected indicationinformation component 684. In some embodiments, the user may interactwith the search/filter component 682 to search through and/orselectively filter the plurality of indications. At least one searchedand/or filtered indication may then be displayed. As illustrated in FIG.34, the search/filter component 682 includes selectable components686-689 (e.g., components that search and/or filter indications based onthat selection) as well as specifiable components 690-692 (e.g.,components that search and/or filter indications based on the dataentered into those components).

As illustrated in FIG. 34, the selected indication information component684 illustrates information associated with a selected indication as at694. It will be appreciated that the selected indication informationcomponent 684 may populate information fields with information from theNDE data and/or inspection information associated with the selectedindication 694 automatically in response to user interaction to selectthe selected indication 694. In addition to the search/filter component682 and the selected indication information component 684, the displayrepresentation 680 may also include controls 696-697 to enlarge, reduce,pan and/or rotate the visual representation of the simulated model 610and the indications. A button to save the current alignment, includingsaving the current zoom level, rotation, searched and/or filteredindications, as well as other alignment information, may be provided asat 698.

Example 5

In some embodiments, it may be advantageous to translate the location ofat least one indication associated with a first part of an asset to asubstantially symmetrical location on a second part of an asset, thesecond part of the asset being substantially symmetrical to the firstpart of the asset. For example, it may be advantageous to viewindications associated with a left wing and a right wing on a simulatedmodel of either the left or right wings. By way of example, FIG. 35 is adisplay representation 700 of the simulated model 610 in which a firstplurality of indications have been visually represented. The simulatedmodel 610 of FIG. 35 is associated with right wing of an aircraft. FIG.36, on the other hand, is a display representation 710 of a simulatedmodel 712 in which a second plurality of indications have been visuallyrepresented. The simulated model 712 of FIG. 36 is associated with aleft wing of an aircraft. It may be advantageous to view the first andsecond plurality of indications on the same simulated model. As such,the locations for each of the second plurality of indications on thesimulated model 712 of FIG. 36 may be transformed to locations on thesimulated model 610. FIG. 37 is a display representation 720illustrating the second plurality of indications at their transformedlocations on the simulated model 610. The second plurality ofindications may then be visually represented with the first plurality ofindications to view trends in indications across the substantiallysymmetrical first and second parts of the asset. To that end, FIG. 38 isa display representation 730 of the first and second plurality ofindications on the simulated model 610.

Although not illustrated, one having ordinary skill in the art willappreciate that the location of one indication may be transformed from asecond part of an asset to a first part of an asset substantiallysymmetrical with the second part. Thus, although Example 5 is disclosedwith regard to a first and second plurality of indications, it will beappreciated that at least one indication for either of the first andsecond parts may be transformed without departing from the scope of theinvention. Moreover, one having ordinary skill in the art willappreciate that indications may be shown on simulated models associatedwith either the first or second substantially similar part consistentwithout departing from the scope of the invention.

Example 6

In some embodiments, it may be advantageous to align a first dataset ofNDE data with a second dataset of NDE data to determine the location ofan indication of a potential problem with either the first or seconddatasets of NDE data. By way of example, FIG. 39 is a diagrammaticillustration of at least a portion of a fan blade 720 of a jet engine.In some configurations, at least a portion of the fan blade 720 ishollow as indicated at void 722. Inasmuch as the fan blade 720 isconfigured to withstand harsh conditions during its use, imperfectionsin the fan blade 720 may be intolerable. As such, after fabrication of afan blade 720, it may be desirable to inspect the fan blade 720. Thus,the fan blade 720 may be inspected by various NDE data collectiondevices.

In some embodiments, the fan blade 720 may be inspected through computedtomography (“CT”) to produce a plurality of slices of the blade thatindicate the external and internal characteristics of that fan blade atthat slice. By way of example, FIG. 40 is a diagrammatic illustration ofthe fan blade 720 and the location of a CT slice as at 730 taken throughthe fan blade 720. FIG. 41 is a display representation of at least aportion of a dataset of NDE data 740 associated with the CT slice 730.As illustrated in FIG. 41, the NDE data 740 may be associated with atleast one indication 742 of a potential problem. In particular, and asillustrated, the indication 742 may be internal to the fan blade 720and, for example, may be an accumulation of material on the innersurface of the fan blade 720.

After capturing the NDE data 740, it may be advantageous to align theNDE data 740 (e.g., first NDE data 740) with additional NDE data (e.g.,second NDE data) and/or a simulated model associated with the fan blade720. Specifically, it may be desirable to associate the first NDE data740 with the second NDE data and determine the location of theindication 742 on both. To associate the datasets of NDE data, aplurality of projections of the first NDE data 740 through the secondNDE data may be generated. By way of example, FIG. 42 is a diagrammaticillustration 750 of a projection of the first NDE data 740 throughsecond NDE data of the fan blade 752 (e.g., an x-ray of the fan blade720 face-on) and a three-dimensional simulated model of the fan blade754 while FIG. 43A and FIG. 43B are respective projections 760 and 765of the first NDE data 740 through the second NDE data 752 illustratingvarious manners in which the first NDE data 740 may be projected throughthe second NDE data 752 and/or simulated model 754. A projection of thefirst NDE data 740 that results in an acceptable alignment on the secondNDE data 752 and/or simulated model 754 may be chosen. In this manner,the location of the indication 742 on the second NDE data 752 and/or thesimulated model 754 may be determined. It will be appreciated that FIGS.39-42 and FIGS. 43A and 43B are merely exemplary to illustrate theprocess to align first NDE data to second NDE data and/or a simulatedmodel associated with both the first and second NDE data. Thus, it willbe appreciated that FIGS. 39-42 and FIGS. 43A and 43B may or may not bedisplayed consistent with embodiments of the invention, and as such theFIGS. 39-42 and FIGS. 43A and 43B are merely illustrated of theapplicants broader inventive concept.

Example 7

In some embodiments, it may be advantageous to distort at least aportion of captured NDE data. By way of example, FIG. 44 is a displayrepresentation of NDE data 770 and FIG. 45 is a display representation780 the aligned NDE data 770 of FIG. 45 aligned with a simulated model610 consistent with embodiments of the invention. However, it may bedetermined that the aligned NDE data 770 does not completely align withthe simulated model 610. As such, and consistent with embodiments of theinvention, at least a portion of the NDE data 770 may be determined tobe locally distorted. As such, the local distortion may be analyzed todetermine whether can be adjusted with a non-rigid alignment algorithmand/or a local deformation algorithm. The local distortion may then beadjusted. FIG. 46 is a display representation of NDE data that has hadat least a portion adjust to account for local distortion 790 (e.g.,“adjusted” NDE data 790). Specifically, FIG. 46 illustrates the NDE data770 of FIG. 45 after it the local distortion thereof has been adjusted.It will be appreciated that the adjusted NDE data 790 may be re-alignedwith the simulated model 610. FIG. 47 is a display representation 800 ofthe adjusted NDE data 790 aligned with the simulated model 610consistent with embodiments of the invention.

While the present invention has been illustrated by a description of thevarious embodiments and the examples, and while these embodiments havebeen described in considerable detail, it is not the intention of theapplicants to restrict or in any way limit the scope of the appendedclaims to such detail. Additional advantages and modifications willreadily appear to those skilled in the art. For example, one havingskill in the art will appreciate that multiple filters may be usedwithout departing from the scope of the invention. Moreover, one havingskill in the art will appreciate that a plurality of datasets of NDEdata from a plurality of portions of a plurality of assets over aplurality of sessions may be filtered as well as searched withoutdeparting from the scope of the invention, and thus embodiments of theinvention should not be limited to the filtering and searching examplesdisclosed herein.

Additionally, and regarding highlighting a lapse in coverage, one havingordinary skill will appreciate that the lapse in coverage may bealternatively indicated other than adjusting a color component of adisplay representation. For example, the lapse in coverage may indicatedby circling or otherwise drawing a line around the boundary of an areaof a display representation associated with missing coverage, applying atexture or pattern to an area of a display representation associatedwith missing coverage, or other manners in highlighting an area of adisplay representation associated with missing coverage. Moreover, andregarding determining missing coverage, one having ordinary skill willappreciate that “coverage” may refer to selective coverage or one ormore portions of an asset. It may be advantageous to merely determine ifspecific parts of a portion of an asset are covered. Thus, “coverage”may refer to whether specific parts of a portion of an asset areassociated with NDE data. For example, it may be advantageous todetermine if specific fasteners associated with a wing of an airplaneare each associated with NDE data. As such, “coverage” of the fastenersmay include datasets of NDE data associated the fasteners and theirimmediate surroundings, but otherwise fail to include portions of thewing that are not salient to the coverage analysis. Thus, one havingskill in the art will appreciate that determining coverage of asimulated model may include determining coverage of one or more distinctportions of the simulated model while ignoring other portions of thesimulated model.

Thus, the invention in its broader aspects is therefore not limited tothe specific details, representative apparatus and method, andillustrative example shown and described. Accordingly, departures may bemade from such details without departing from the spirit or scope ofapplicants' general inventive concept.

1. A method of managing non-destructive evaluation (NDE) data in asystem of the type that includes at least one processing unit and amemory, the method comprising: receiving NDE data for at least a portionof an asset, including receiving inspection information associated withthe at least a portion of the asset; determining at least one alignmentalgorithm to align the NDE data to a simulated model of the at least aportion of the asset based upon at least one of the NDE data and theinspection information; automatically aligning the NDE data to thesimulated model with the at least one alignment algorithm; generating adisplay representation that visually represents the aligned NDE data onthe simulated model; and exporting information associated with thealigned NDE data for analysis.
 2. A method of managing non-destructiveevaluation (NDE) data in a system of the type that includes at least oneprocessing unit and a memory, the method comprising: receiving first NDEdata for at least a portion of an asset, including receiving firstinspection information associated with the at least a portion of theasset; receiving second NDE data for the at least a portion of theasset, including receiving second inspection information associated withthe at least a portion of the asset; determining a first alignmentalgorithm to align the first NDE data to a simulated model of the atleast a portion of the asset based upon at least one of the first NDEdata and the first inspection information; determining a secondalignment algorithm to align the second NDE data to the simulated modelof the at least a portion of the asset based upon at least one of thesecond NDE data and the second inspection information; automaticallyaligning the first NDE data and second NDE data to the simulated modelwith the respective first alignment algorithm and second alignmentalgorithm; determining a first indication of a first potential problemassociated with the first NDE data based upon the first inspectioninformation; determining a second indication of a second potentialproblem associated with the second NDE data based upon the secondinspection information; and generating a display representation thatvisually represents the first indication and the second indication onthe simulated model.
 3. The method of claim 2, further comprising:determining a first and second location for the respective first andsecond indications on the simulated model based upon the respectivefirst and second inspection information.
 4. The method of claim 3,wherein generating the display representation further comprises:visually representing an uncertainty of the determined first locationfor the first indication; and visually representing an uncertainty ofthe determined second location for the second indication.
 5. The methodof 4, wherein visually representing the uncertainty of the determinedfirst location includes visually representing a probability density of adistance from the determined first location for the first indication. 6.The method of claim 5, wherein the probability density of the distancefrom the determined first location for the first indication is aGaussian distribution of the distance from the determined first locationfor the first indication.
 7. The method of claim 4, wherein visuallyrepresenting the uncertainty of the determined first location includesadjusting a color component of at least a portion of the displayrepresentation to indicate the uncertainty of the determined firstlocation for the first indication.
 8. The method of claim 4, furthercomprising: exporting information associated with the uncertainty of thedetermined first and second locations for the respective first andsecond indications for analysis.
 9. A method of managing non-destructiveevaluation (NDE) data in a system of the type that includes at least oneprocessing unit and a memory, the method comprising: receiving first NDEdata for at least a portion of a first asset, including receiving firstinspection information associated with the at least a portion of thefirst asset; receiving second NDE data for the at least a portion of asecond asset, including receiving second inspection informationassociated with the at least a portion of the second asset; retrieving asimulated model associated with the at least a portion of the firstasset and the at least a portion of the second asset; determining afirst alignment algorithm to align the first NDE data to the simulatedmodel based upon at least one of the first NDE data and the firstinspection information; determining a second alignment algorithm toalign the second NDE data to the simulated model based upon at least oneof the second NDE data and the second inspection information;automatically aligning the first NDE data and second NDE data to thesimulated model with the respective first alignment algorithm and secondalignment algorithm; determining a first indication of a first potentialproblem associated with the first NDE data based upon the firstinspection information; determining a second indication of a secondpotential problem associated with the second NDE data based upon thesecond inspection information; and generating a display representationthat visually represents the first indication and the second indicationon the simulated model.
 10. The method of claim 9, further comprising:in response to interaction with the visual representation of the firstindication, visually representing the first indication and the alignedfirst NDE data on the simulated model.
 11. The method of claim 9,further comprising: determining a first and second location for therespective first and second indications on the simulated model basedupon the respective first and second inspection information.
 12. Themethod of claim 11, wherein generating the display representationfurther comprises: visually representing an uncertainty of thedetermined first location for the first indication; and visuallyrepresenting an uncertainty of the determined second location for thesecond indication.
 13. The method of 12, wherein visually representingthe uncertainty of the determined first location includes visuallyrepresenting a probability density of a distance from the determinedfirst location for the first indication.
 14. The method of claim 13,wherein the probability density of the distance from the determinedfirst location for the first indication is a Gaussian distribution ofthe distance from the determined first location for the firstindication.
 15. The method of claim 12, wherein visually representingthe uncertainty of the determined first location includes adjusting acolor component of at least a portion of the display representation toindicate the uncertainty of the determined first location for the firstindication.
 16. The method of claim 12, further comprising: exportinginformation associated with the uncertainty of the determined first andsecond locations for the respective first and second indications foranalysis.
 17. A method of managing non-destructive evaluation (NDE) datain a system of the type that includes at least one processing unit and amemory, the method comprising: receiving NDE data for at least a portionof an asset, including receiving inspection information associated withthe at least a portion of the asset; selecting a shot descriptor datastructure from among a plurality of shot descriptor data structuresbased upon at least one of the NDE data and the inspection information;determining whether additional NDE data is required to complete analignment of the NDE data to a simulated model of the at least a portionof the asset based upon the shot descriptor data structure; and inresponse to determining additional NDE data is required to complete thealignment of the NDE data to the simulated model, providing a messagethat additional NDE data is required to complete the alignment of theNDE data to the simulated model.
 18. The method of claim 17, wherein theNDE data is first NDE data and wherein the inspection information isfirst inspection information, the method further comprising: receivingsecond NDE data for the at least a portion of the asset, includingreceiving second inspection information associated with the at least aportion of the asset; determining at least one alignment algorithm toalign the first NDE data and the second NDE data to the simulated modelbased upon the shot descriptor data structure; in response to receivingthe second NDE data, automatically aligning the first NDE data and thesecond NDE data to the simulated model with the at least one alignmentalgorithm; and generating a display representation that visuallyrepresents the aligned first NDE data and the aligned second NDE data onthe simulated model.
 19. A method of managing non-destructive evaluation(NDE) data in a system of the type that includes at least one processingunit and a memory, the method comprising: retrieving NDE data for atleast a portion of an asset, the NDE data having previously been alignedto a simulated model of the at least a portion of the asset; andexporting information associated with the retrieved NDE data foranalysis.
 20. The method of claim 19, wherein the NDE data is retrievedin response to a request from a user to retrieve the NDE data.
 21. Themethod of claim 19, further comprising: retrieving the simulated model;and generating a display representation that visually represents theretrieved NDE data aligned on the simulated model.
 22. The method ofclaim 19, further comprising: retrieving an indication of a potentialproblem associated with the first NDE data, including retrieving adetermined location for the indication on the simulated model.
 23. Themethod of claim 22, wherein exporting information associated with theretrieved NDE data includes exporting information associated with theretrieved indication.